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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
PERBANDINGAN ALGORITMA DATA MINING NAÏVE BAYES DAN BAYES NETWORK UNTUK MENGIDENTIFIKASI PENYAKIT TIROID
Keywords:
Classification model, Naive Bayes, ROC curve, Bayes Network, hyperthyroid, Data Mining, Penyakit Tiroid
Abstract
In data mining, a known Classification model that can be used to identify thyroid disease is Naive Bayes and Bayes Network methods. In this study, a model is made by using both algorithms. the data used are taken from the data of Patients with thyroid by using the tools KNIME. The model then compared to determine the best algorithm in the determination of disease identification. To measure the performance of the two algorithms, it used methods of testing of cross-validation and split percentage. The measurement results using confusion matrix and ROC curves. By using the confusion matrix, Bayes Network has higher accuracy with 98,491% compared with the Naive Bayes with 91,803%. Using the ROC curve, Bayes Network also has higher accuracy with the ROC curve - negative (0.9337), ROC - hyperthyroid (0.9933) and ROC - hypothyroid (0.9977). while Naive Bayes with ROC curve - negative (0.8760), ROC - hyperthyroid (0.9789) and ROC - hypothyroid (0.9018). The method which has very good classification is sequentially Bayes network and naïve Bayes based on assessment AUC between 0.90-1.00. thus the Bayes Network algorithm can provide solutions to the problems of identifying thyroid disease.
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Hamdani, W., & Sampepajung, D. (2010). Thyroid Cancer : the Diagnose and the Management.
Hannan, S. A., Manza, R. R., & Ramteke, R. J. (2010). Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis. International Journal of Computer Applications, 7(13), 975–8887. https://doi.org/10.5120/1325-1799
Kirschen, R. H., O’Higgins, E. A., & Lee, R. T. (2000). The Royal London Space Planning: An integration of space analysis and treatment planning. American Journal of Orthodontics and Dentofacial Orthopedics, 118(4), 448–455. https://doi.org/10.1067/mod.2000.109031
Kothari, C. (2004). Research methodology: methods and techniques. New Age International.
Neshat, M., & Yaghobi, M. (2009). Designing a Fuzzy Expert System of Diagnosing the Hepatitis B Intensity Rate and Comparing it with Adaptive Neural Network Fuzzy System, II.
Panda, M., & Patra, M. (2007). Network intrusion detection using naive bayes. … Journal of Computer Science and Network Security, 7(12), 258–263.
Sarwar, A. (2012). abid savar-Intelligent Naïve Bayes Approach to Diagnose-2012.pdf, (November), 14–16.
Tandra, H. (2011). Mencegah Dan Mengatasi Penyakit Tiroid. Jakarta: Gramedia.
Wang, L., Cao, F., Wang, S., Sun, M., & Dong, L. (2017). Using k-dependence causal forest to mine the most significant dependency relationships among clinical variables for thyroid disease diagnosis. Plos ONE, 12(8), e0182070. https://doi.org/10.1371/journal.pone.0182070
Werner, D. (2010). Apa yang Anda Kerjakan Bila Tidak Ada Dokter. (Andi Offset, Ed.). Yogyakarta.
Who, M. (2007). Scaling up prevention and control of noncommunicable diseases : The SEANET-NCD meeting, (October), 22–26.
Wijonarko, B. (2017). Laporan Akhir Penelitian Mandiri. Jakarta.
Published
2018-03-15
How to Cite
Wijonarko, B. (2018). PERBANDINGAN ALGORITMA DATA MINING NAÏVE BAYES DAN BAYES NETWORK UNTUK MENGIDENTIFIKASI PENYAKIT TIROID. Jurnal Pilar Nusa Mandiri, 14(1), 21-26. https://doi.org/10.33480/pilar.v14i1.83
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